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The drive to encourage young people to pursue degrees and careers in engineering has led to an increase in student populations in engineering programs. For some institutions, such as large public research institutions, this has led to large class sizes for courses that are commonly taken across multiple programs. While this decision is reasonable from an operational and resource management perspective, research on large classes have shown that students suffer decreased engagement, motivation and achievement. Instructors, on the other hand, report having difficulty establishing rapport with their students and a growing inability to monitor students’ learning gains and provide quality individualized feedback. To address these issues, our project draws from Lattuca and Stark’s Academic Plan model, which incorporates a thorough consideration of factors influencing curricular activities that can be applied at the course, program, and institutional levels, and assumes that instructors are key actors in curriculum development and revision. We aim to revitalize feedback loops to help instructors and departments continuously improve. Recognizing that we must understand both individual and systems level perspectives, we prioritize regular engagement between faculty and institutional support structures to collaboratively identify problems and systematically establish continuous improvement. In the first phase of this NSF IUSE Institutional Transformation project, we focus on specifically prompting and studying the experiences of 8 instructors of foundational engineering courses usually taught in large class sizes across 4 different departments at a large public research institution. We collected qualitative data (semi-structured interviews, reflective journals, course-related documents) and quantitative data (student surveys and institution-provided transcript data) to answer research questions (e.g., what data do faculty teaching large foundational undergraduate engineering courses identify as being useful so that they may enhance students’ experiences and outcomes within the classes that they teach and across students’ multiple large classes?) at the intersection of learning analytics and faculty change. The data was used as a baseline to further refine data collection protocols, identify data that faculty consider meaningful and useful for managing large foundational engineering courses, and consider ways of productively leveraging institutional data to improve the learning experience in these courses. Data collection for the first phase is ongoing and will continue through the Spring 2018 semester. Findings for this paper will include high-level insights from Fall interviews with instructors as well as data visualizations created from the population-level data characterizing student performance in the foundational courses within the context of pre-college characteristics (e.g., SAT scores) and/or other academic outcomes (e.g., major switching within or out of engineer, degree attainment).
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Reclaiming the Large Foundational Engineering Classroom: Instructors’ Needs and Aspirations
This Work-In-Progress research paper presents preliminary results and next steps of a study that aims to identify institutional data and resources that instructors find helpful in facilitating learning in large foundational engineering courses. The work is motivated by resource-driven compromises made in response to increasing engineering student populations. One such compromise is teaching some courses (usually foundational courses taken by students across multiple disciplines) in large sections, despite research suggesting that large class environments may correspond with unfavorable student learning experiences. Examples of courses often taught in large class environments are mathematics, physics, and mechanics. We are currently working with a cohort of instructors of foundational engineering courses as part of an NSF Institutional Transformation project. We have collected qualitative data through semi-structured interviews to explore the following research question: What data and/or resources do STEM faculty teaching large foundational classes for undergraduate engineering identify as being useful to enhance students' experiences and outcomes a) within the classes that they teach, and b) across the multiple large foundational engineering classes taken by students? Our inquiry and analysis are guided by Lattuca and Stark's Academic Plan Model. Preliminary analysis indicated that instructors would like more opportunities to interact and collaborate with instructors from other departments. These results will inform activities for our Large Foundational Courses Summit scheduled for Summer 2018 as part of the project.
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- Award ID(s):
- 1712089
- PAR ID:
- 10100377
- Date Published:
- Journal Name:
- 2018 IEEE Frontiers in Education Conference (FIE)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/FIE.2018.8659006
